LEARNING OBSTACLES IN DESCRIPTIVE STATISTICS: CHALLENGES IN STATISTICAL THINKING FOR PROSPECTIVE MATHEMATICS TEACHERS
DOI:
https://doi.org/10.24127/ajpm.v15i2.14702Keywords:
descriptive statistics, learning obstacle, statistical thinkingAbstract
Statistical thinking is an essential competency for prospective mathematics teachers, as it enables them to understand, organize, represent, analyze, and interpret data effectively. However, many students still experience difficulties in understanding and applying descriptive statistics concepts, which may hinder the development of their statistical thinking skills. Therefore, identifying learning obstacles is necessary as a basis for designing more effective learning experiences. This study aims to identify the learning obstacles encountered by prospective mathematics teachers in descriptive statistics courses. The study employed Didactical Design Research (DDR) using a qualitative methodology within an interpretive paradigm. The participants were 38 prospective mathematics teachers enrolled at a private university in Bandung, Indonesia. The research instruments consisted of a statistical thinking test, semi-structured interview guidelines, and document analysis. Data were collected through tests, interviews, and document analysis and were analyzed through the processes of identification, classification, reduction, and verification. Data trustworthiness was established through methodological and source triangulation. The findings revealed that students experienced difficulties across all indicators of statistical thinking, including describing, organizing, representing, analyzing, and interpreting data. These difficulties were reflected in misconceptions regarding the relationship between mean, median, mode, and distribution skewness, the application of measures of position, the interpretation of data variability, and the analysis of relative performance. The study identified ontogenic, epistemological, and didactical obstacles that hindered the development of students’ statistical thinking. These findings provide an empirical basis for developing a hypothetical learning trajectory to enhance statistical thinking in descriptive statistics courses.
References
Artigue, M. (2009). Didactical design in mathematics education. Brill Sense. https://doi.org/https://doi.org/10.1163/9789087907839_003
Azis, A., & Dahlan, J. A. (2024). Statistical Literacy Level of Mathematics Education Students : Challenges and Recommendations for Competency Improvement. Alifmatika: Jurnal Pendidikan Dan Pembelajaran Matematika, 6(2), 263–278. https://doi.org/10.35316/alifmatika.2024.v6i2.263-278
Azmay, N. A. M. N., Rosli, R., Maat, S. M., & Mahmud, M. S. (2023). Educational Research Trends on Statistical Reasoning and Statistical Thinking : A Systematic Literature Review. International Journal Of Academic Research In Progressive Education And Development, 12(2), 543–556. https://doi.org/10.6007/IJARPED/v12-i2/16921
Ben-Zvi, D., & Makar, K. (2016). International Perspectives on the Teaching and Learning of Statistics BT - The Teaching and Learning of Statistics: International Perspectives. In D. Ben-Zvi & K. Makar (Eds.), The Teaching and Learning of Statistics (pp. 1–10). Springer International Publishing. https://doi.org/10.1007/978-3-319-23470-0_1
Boels, L., Bakker, A., Dooren, W. Van, & Drijvers, P. (2019). Conceptual difficulties when interpreting histograms : A review. Educational Research Review, 28(October 2018), 100291. https://doi.org/10.1016/j.edurev.2019.100291
Brousseau, G. (2002). Theory Of Didactical Situations In Mathematics Didactique Des Mathématiques, 1970–1990. Kluwer Academic Publishers.
Chaphalkar, R., & Wu, K. (2020). Students ’ Reasoning about Variability in Graphs during an Introductory Statistics Course. International Electronic Journal of Mathematics Education, 15(2), 1–22. https://doi.org/https://doi.org/10.29333/iejme/7602
Cipta, E. S., Suryadi, D., Herman, T., & Jupri, A. (2024). The utilization of technology for optimizing statistical thinking: Global trends and directions for future research. AIP Conference Proceedings, 3220(1). https://doi.org/https://doi.org/10.1063/5.0235077
Cipta, E. S., Suryadi, D., Herman, T., & Jupri, A. (2026). The Effect Of Digital Didactic Intervention On Measures Of Central Tendency Towards Students ’ Statistical Thinking. 9(1), 164–176. https://doi.org/https://dx.doi.org/10.22460/jiml.v9i1.p29891
Cipta, E. S., Suryadi, D., Herman, T., Jupri, A., Konidah, E., Chusaery, H. A. L., Haryadi, H., & Sarinah, S. (2024). Enhancing Students’ Understanding of the Mean Concept Through Problem-Based Learning Assisted By Geogebra: a Quasi-Experimental Study. Journal of Engineering Science and Technology, 19(5), 18–26. https://jestec.taylors.edu.my/Special Issue ISCoE 2023_3/ISCoE 2023 3_03.pdf
Del-Callejo-Canal, D., Canal-Martínez, M., & Hákim-Krayem, M. R. (2020). Statistical thinking development in superior level students through one educative experience. Educacion Matematica, 6(2), 194–216. https://doi.org/10.24844/EM3202.08
Engel, J. (2017). Statistical Literacy For Active Citizenship : A Call For Data Science Education. Statistics Education Research Journal, 16(1), 44–49. https://doi.org/http://iase-web.org/Publications.php?p=SERJ
Fielding, J., Makar, K., & Ben, D. (2025). Developing students ’ reasoning with data and data ‑ ing. ZDM – Mathematics Education, 57(1), 1–18. https://doi.org/10.1007/s11858-025-01671-6
Friedrich, A., Schreiter, S., Vogel, M., Genschow, S. B., Brünken, R., Kuhn, J., Lehmann, J., & Malone, S. (2024). What shapes statistical and data literacy research in K ‑ 12 STEM education ? A systematic review of metrics and instructional strategies. International Journal of STEM Education. https://doi.org/10.1186/s40594-024-00517-z
Frischemeier, D., & Schnell, S. (2023). Statistical investigations in primary school – the role of contextual expectations for data analysis. Mathematics Education Research Journal, 35(1), 217–242. https://doi.org/10.1007/s13394-021-00396-5
Garfield, J., Ben-Zvi, D., Chance, B., Medina, E., Roseth, C., & Zieffler, A. (2008). Developing Students’ Statistical Reasoning: Connecting Research and Teaching Practice. In Springer. https://doi.org/10.1007/978-1-4020-8383-9
Gravemeijer, K. P. E. (1994). Developing Realistic Mathematics Education.
Hasim, S. M., & Rosli, R. (2024). A Systematic Review on Teaching Strategies for Fostering Students ’ Statistical Thinking. International Journal of Learning, Teaching and Educational Research, 23(1), 136–158. https://doi.org/https://doi.org/10.26803/ijlter.23.1.8
Hidayat, W., Widodo, S. A., & Syahrizal, T. (2023). The statistical thinking skill and adversity quotient of English pre-service teacher. International Journal of Evaluation and Research in Education, 12(1), 421–432. https://doi.org/10.11591/ijere.v12i1.24302
Moleong. (2007). Metodologi penelitian kualitatif. PT Remaja Rosdakarya.
Naeem, M., Ozuem, W., Howell, K., & Ranfagni, S. (2024). A Step-by-Step Process of Thematic Analysis to Develop a Conceptual Model in Qualitative Research. International Journal of Qualitative Methods, 22(October), 1–18. https://doi.org/10.1177/16094069231205789
Oslington, G., Mulligan, J., & Bergen, P. Van. (2024). Shifts in Students’ Predictive Reasoning from Data Tables in Years 3 and 4. Mathematics Education Research Journal, 36(3), 547–576. https://doi.org/10.1007/s13394-023-00460-2
Patton, M. Q. (2022). Qualitative research and evaluation methods (3rd ed.). SAGE Publications.
Prodromou, T., & Dunne, T. (2017). Statistical literacy in data revolution era: Building blocks and instructional dilemmas. Statistics Education Research Journal, 16(1), 38–43. https://doi.org/10.52041/serj.v16i1.212
Rohimah, S. M., Putri, S. A., & Helsa, Y. (2026). Conceptual and procedural errors of pre-service elementary teachers in solving educational research statistics problems. Jurnal Elemen, 12(1), 49–65. https://doi.org/https:/doi.org/10.29408/jel.v12i1.32327
Şahal, M., & Koklu, O. (2026). Examining Pre ‑ service Mathematics Teachers ’ Statistical Investigations : Challenges and Instructional Support. In International Journal of Science and Mathematics Education (Vol. 24, Issue 19). Springer Nature Singapore. https://doi.org/10.1007/s10763-025-10634-y
Sanchez, J. M. (2023). The Need to Reinforce the Teaching of Basic Descriptive Statistics Required in Reporting Quantitative Laboratory Results: Diagnose of Common Students’ Misconceptions. Journal of Chemical Education, 100(7), 2713–2718. https://doi.org/10.1021/acs.jchemed.3c00394
Schreiter, S., Friedrich, A., Fuhr, H., Malone, S., Brünken, R., Kuhn, J., & Vogel, M. (2024). Teaching for statistical and data literacy in K ‑ 12 STEM education : a systematic review on teacher variables , teacher education , and impacts on classroom practice. ZDM – Mathematics Education, 56(1), 31–45. https://doi.org/10.1007/s11858-023-01531-1
Schreiter, S., & Vogel, M. (2025). Students ’ local vs . global views of data distributions : a cross ‑ grade ‑ level analysis using eye ‑ tracking. Educational Studies in Mathematics, 119, 1–22. https://doi.org/10.1007/s10649-024-10352-2
Setiawan, E. P., & Sukoco, H. (2021). Exploring First Year University Students’ Statistical Literacy: A Case on Describing and Visualizing Data. Journal on Mathematics Education, 12(3), 427–448. https://doi.org/http://doi.org/10.22342/jme.12.3.13202.427-448
Silva, P. N., Sarnecka, B. W., & Sarnecka, B. W. (2025). What Do Your Students Struggle with ? A Survey of Statistics Instructors What Do Your Students Struggle with ? A Survey of Statistics Instructors ABSTRACT. Journal of Statistics and Data Science Education, 1–12. https://doi.org/10.1080/26939169.2025.2455560
Sugiyono. (2022). Metode Penelitian Kuantitatif, Kualitatif, Dan R&D. Alfabeta.
Suryadi, D. (2019). Landasan Filosofis Penelitian Desain Didaktis (DDR). Pusat Pengembangan DDR Indonesia.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 AKSIOMA: Jurnal Program Studi Pendidikan Matematika
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with AKSIOMA: Jurnal Program Studi Pendidikan Matematika agree to the following terms:
Creative Commons License
AKSIOMA: Jurnal Program Studi Pendidikan Matematika is licensed under a Creative Commons Attribution 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. AKSIOMA offers all authors of journal articles allows their research openly available, free access and time restrictions.
All articles published Open Access will be immediately and permanently free for everyone to read and download. Under the CC-BY license, authors retain ownership of the copyright for their article, but authors grant others permission to use the content of publications in AKSIOMA in whole or in part provided that the original work is properly cited. Users (redistributors) of AKSIOMA are required to cite the original source, including the author's names, AKSIOMA as the initial source of publication, year of publication, volume number and DOI (if available).
.png)
.png)
